{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "718464bc-d3bc-4231-ae00-2a3bff2fab68",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import xgboost as xgb\n",
    "import seaborn as sns \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import numpy as np\n",
    "np.int = int\n",
    "import matplotlib.pyplot as plt\n",
    "#导入各种用于评估模型性能和参数优化的函数和类，例如均方误差（MSE）、均绝对误差（MAE）、决定系数（R2 Score）、解释方差分（explained variance score）等\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.metrics import mean_squared_error, r2_score,explained_variance_score,mean_absolute_percentage_error,mean_absolute_error\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "#导入XGBoost库中的XGBClassifier类，用于建立XGBoost分类模型，并导入plot_importance函数用于特征重要性可视化。\n",
    "from xgboost import XGBClassifier, plot_importance\n",
    "#导入互信息函数\n",
    "from sklearn.feature_selection import mutual_info_regression\n",
    "#导入lasso模型筛选特征变量\n",
    "from sklearn.linear_model import Lasso\n",
    "#导入Scikit-Optimize库中的贝叶斯优化相关的函数和类，用于超参数调优\n",
    "from skopt import gp_minimize\n",
    "from skopt.space import Real, Integer\n",
    "#导入交叉验证函数，用于评估模型性能\n",
    "from sklearn.model_selection import cross_val_score\n",
    "#导入随机森林回归模型\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "plt.rcParams['font.family'] = 'Times New Roman'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "50045870-09f1-4bd7-b236-4bafb6c90da6",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#df = pd.read_excel('指标.xlsx')\n",
    "d = pd.read_excel(r'D:\\work\\暑期工作\\0606_重新处理\\数据保存\\特征50 (无).xlsx')\n",
    "dd = pd.read_excel(r'D:\\work\\暑期工作\\0606_重新处理\\数据保存\\特501(无).xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fc42cdc5-0483-4439-98af-3593081eff03",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "d=d.drop(columns=['Unnamed: 0'])\n",
    "dd=dd.drop(columns=['Unnamed: 0'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "59285029-518e-41c4-8d69-a0f2c2da0cba",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NAME</th>\n",
       "      <th>AvgSurfT_inst_chengshu</th>\n",
       "      <th>LST_Day_1km_chousui</th>\n",
       "      <th>LST_Night_1km_fennie</th>\n",
       "      <th>soil_temperature_level_2_kaihua</th>\n",
       "      <th>soil_temperature_level_2_guanjiang</th>\n",
       "      <th>soil_temperature_level_2_shouhuo</th>\n",
       "      <th>soil_temperature_level_3_chousui</th>\n",
       "      <th>soil_temperature_level_3_kaihua</th>\n",
       "      <th>soil_temperature_level_3_guanjiang</th>\n",
       "      <th>...</th>\n",
       "      <th>VDVI_shouhuo</th>\n",
       "      <th>WDRVI_bajie</th>\n",
       "      <th>WDRVI_yunsui</th>\n",
       "      <th>WDRVI_chousui</th>\n",
       "      <th>WDVI_bajie</th>\n",
       "      <th>WDVI_yunsui</th>\n",
       "      <th>WDVI_chousui</th>\n",
       "      <th>WDVI_kaihua</th>\n",
       "      <th>wet_kaihua</th>\n",
       "      <th>亩产</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>410522</td>\n",
       "      <td>301.830072</td>\n",
       "      <td>14770.448120</td>\n",
       "      <td>13653.260039</td>\n",
       "      <td>291.679432</td>\n",
       "      <td>294.485981</td>\n",
       "      <td>301.239969</td>\n",
       "      <td>287.263466</td>\n",
       "      <td>288.780851</td>\n",
       "      <td>290.517675</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006043</td>\n",
       "      <td>-0.270294</td>\n",
       "      <td>0.007577</td>\n",
       "      <td>0.038094</td>\n",
       "      <td>0.312649</td>\n",
       "      <td>0.346212</td>\n",
       "      <td>0.380784</td>\n",
       "      <td>0.355850</td>\n",
       "      <td>-0.090609</td>\n",
       "      <td>7467.000138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>410421</td>\n",
       "      <td>301.881276</td>\n",
       "      <td>14811.998245</td>\n",
       "      <td>13724.830174</td>\n",
       "      <td>291.624652</td>\n",
       "      <td>294.462918</td>\n",
       "      <td>301.229017</td>\n",
       "      <td>287.616328</td>\n",
       "      <td>289.032764</td>\n",
       "      <td>290.784441</td>\n",
       "      <td>...</td>\n",
       "      <td>0.010118</td>\n",
       "      <td>-0.220879</td>\n",
       "      <td>-0.005646</td>\n",
       "      <td>0.020097</td>\n",
       "      <td>0.272293</td>\n",
       "      <td>0.321097</td>\n",
       "      <td>0.313054</td>\n",
       "      <td>0.308834</td>\n",
       "      <td>-0.088144</td>\n",
       "      <td>5972.666001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>411726</td>\n",
       "      <td>298.990822</td>\n",
       "      <td>15035.873561</td>\n",
       "      <td>13760.252546</td>\n",
       "      <td>291.141219</td>\n",
       "      <td>293.187955</td>\n",
       "      <td>298.777454</td>\n",
       "      <td>287.204259</td>\n",
       "      <td>288.763693</td>\n",
       "      <td>290.271875</td>\n",
       "      <td>...</td>\n",
       "      <td>0.038036</td>\n",
       "      <td>-0.273682</td>\n",
       "      <td>-0.141936</td>\n",
       "      <td>-0.176015</td>\n",
       "      <td>0.255247</td>\n",
       "      <td>0.291260</td>\n",
       "      <td>0.276982</td>\n",
       "      <td>0.277341</td>\n",
       "      <td>-0.107184</td>\n",
       "      <td>5687.791498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>410822</td>\n",
       "      <td>302.326105</td>\n",
       "      <td>14909.855104</td>\n",
       "      <td>13684.031442</td>\n",
       "      <td>291.393057</td>\n",
       "      <td>294.128413</td>\n",
       "      <td>300.920627</td>\n",
       "      <td>286.732262</td>\n",
       "      <td>288.209019</td>\n",
       "      <td>290.136585</td>\n",
       "      <td>...</td>\n",
       "      <td>0.017507</td>\n",
       "      <td>-0.305916</td>\n",
       "      <td>-0.086524</td>\n",
       "      <td>0.009030</td>\n",
       "      <td>0.277992</td>\n",
       "      <td>0.313744</td>\n",
       "      <td>0.335973</td>\n",
       "      <td>0.327689</td>\n",
       "      <td>-0.066776</td>\n",
       "      <td>7959.597222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>411082</td>\n",
       "      <td>302.665974</td>\n",
       "      <td>14764.439462</td>\n",
       "      <td>13685.820730</td>\n",
       "      <td>292.812644</td>\n",
       "      <td>295.863324</td>\n",
       "      <td>302.456445</td>\n",
       "      <td>288.378758</td>\n",
       "      <td>289.948855</td>\n",
       "      <td>291.813767</td>\n",
       "      <td>...</td>\n",
       "      <td>0.009523</td>\n",
       "      <td>-0.155182</td>\n",
       "      <td>0.049857</td>\n",
       "      <td>0.027083</td>\n",
       "      <td>0.329961</td>\n",
       "      <td>0.356682</td>\n",
       "      <td>0.342203</td>\n",
       "      <td>0.353976</td>\n",
       "      <td>-0.074036</td>\n",
       "      <td>7662.496889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>411081</td>\n",
       "      <td>302.379726</td>\n",
       "      <td>14864.959928</td>\n",
       "      <td>13716.550674</td>\n",
       "      <td>292.036420</td>\n",
       "      <td>294.927847</td>\n",
       "      <td>301.419777</td>\n",
       "      <td>287.740673</td>\n",
       "      <td>289.256381</td>\n",
       "      <td>291.034992</td>\n",
       "      <td>...</td>\n",
       "      <td>0.014550</td>\n",
       "      <td>-0.222630</td>\n",
       "      <td>-0.047730</td>\n",
       "      <td>-0.033628</td>\n",
       "      <td>0.276777</td>\n",
       "      <td>0.307460</td>\n",
       "      <td>0.306785</td>\n",
       "      <td>0.308283</td>\n",
       "      <td>-0.090942</td>\n",
       "      <td>6613.422500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>411424</td>\n",
       "      <td>302.404313</td>\n",
       "      <td>14767.178119</td>\n",
       "      <td>13698.554350</td>\n",
       "      <td>293.142870</td>\n",
       "      <td>296.125191</td>\n",
       "      <td>302.061064</td>\n",
       "      <td>288.739608</td>\n",
       "      <td>290.273988</td>\n",
       "      <td>292.107970</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006859</td>\n",
       "      <td>-0.049587</td>\n",
       "      <td>0.177212</td>\n",
       "      <td>0.143995</td>\n",
       "      <td>0.356591</td>\n",
       "      <td>0.372768</td>\n",
       "      <td>0.380640</td>\n",
       "      <td>0.392420</td>\n",
       "      <td>-0.074186</td>\n",
       "      <td>7579.469809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>411724</td>\n",
       "      <td>300.297286</td>\n",
       "      <td>14800.494981</td>\n",
       "      <td>13753.180467</td>\n",
       "      <td>291.750152</td>\n",
       "      <td>293.718662</td>\n",
       "      <td>299.219180</td>\n",
       "      <td>287.779530</td>\n",
       "      <td>289.413163</td>\n",
       "      <td>290.932713</td>\n",
       "      <td>...</td>\n",
       "      <td>0.021031</td>\n",
       "      <td>0.082507</td>\n",
       "      <td>0.214809</td>\n",
       "      <td>0.149694</td>\n",
       "      <td>0.389395</td>\n",
       "      <td>0.347135</td>\n",
       "      <td>0.345173</td>\n",
       "      <td>0.381115</td>\n",
       "      <td>-0.079914</td>\n",
       "      <td>5754.140236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>411324</td>\n",
       "      <td>300.250438</td>\n",
       "      <td>14870.302144</td>\n",
       "      <td>13755.946153</td>\n",
       "      <td>291.086794</td>\n",
       "      <td>293.369445</td>\n",
       "      <td>300.014684</td>\n",
       "      <td>287.141116</td>\n",
       "      <td>288.584741</td>\n",
       "      <td>290.356689</td>\n",
       "      <td>...</td>\n",
       "      <td>0.027628</td>\n",
       "      <td>-0.102704</td>\n",
       "      <td>0.058565</td>\n",
       "      <td>0.005744</td>\n",
       "      <td>0.309025</td>\n",
       "      <td>0.317261</td>\n",
       "      <td>0.348503</td>\n",
       "      <td>0.307512</td>\n",
       "      <td>-0.097445</td>\n",
       "      <td>5467.128359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>410122</td>\n",
       "      <td>302.101448</td>\n",
       "      <td>14900.316284</td>\n",
       "      <td>13728.293344</td>\n",
       "      <td>292.494994</td>\n",
       "      <td>295.405081</td>\n",
       "      <td>301.660649</td>\n",
       "      <td>288.002027</td>\n",
       "      <td>289.590761</td>\n",
       "      <td>291.427115</td>\n",
       "      <td>...</td>\n",
       "      <td>0.035895</td>\n",
       "      <td>-0.356993</td>\n",
       "      <td>-0.162055</td>\n",
       "      <td>-0.172850</td>\n",
       "      <td>0.260427</td>\n",
       "      <td>0.290878</td>\n",
       "      <td>0.283795</td>\n",
       "      <td>0.284526</td>\n",
       "      <td>-0.095178</td>\n",
       "      <td>6637.200003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>102 rows × 95 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       NAME  AvgSurfT_inst_chengshu  LST_Day_1km_chousui   \n",
       "0    410522              301.830072         14770.448120  \\\n",
       "1    410421              301.881276         14811.998245   \n",
       "2    411726              298.990822         15035.873561   \n",
       "3    410822              302.326105         14909.855104   \n",
       "4    411082              302.665974         14764.439462   \n",
       "..      ...                     ...                  ...   \n",
       "97   411081              302.379726         14864.959928   \n",
       "98   411424              302.404313         14767.178119   \n",
       "99   411724              300.297286         14800.494981   \n",
       "100  411324              300.250438         14870.302144   \n",
       "101  410122              302.101448         14900.316284   \n",
       "\n",
       "     LST_Night_1km_fennie  soil_temperature_level_2_kaihua   \n",
       "0            13653.260039                       291.679432  \\\n",
       "1            13724.830174                       291.624652   \n",
       "2            13760.252546                       291.141219   \n",
       "3            13684.031442                       291.393057   \n",
       "4            13685.820730                       292.812644   \n",
       "..                    ...                              ...   \n",
       "97           13716.550674                       292.036420   \n",
       "98           13698.554350                       293.142870   \n",
       "99           13753.180467                       291.750152   \n",
       "100          13755.946153                       291.086794   \n",
       "101          13728.293344                       292.494994   \n",
       "\n",
       "     soil_temperature_level_2_guanjiang  soil_temperature_level_2_shouhuo   \n",
       "0                            294.485981                        301.239969  \\\n",
       "1                            294.462918                        301.229017   \n",
       "2                            293.187955                        298.777454   \n",
       "3                            294.128413                        300.920627   \n",
       "4                            295.863324                        302.456445   \n",
       "..                                  ...                               ...   \n",
       "97                           294.927847                        301.419777   \n",
       "98                           296.125191                        302.061064   \n",
       "99                           293.718662                        299.219180   \n",
       "100                          293.369445                        300.014684   \n",
       "101                          295.405081                        301.660649   \n",
       "\n",
       "     soil_temperature_level_3_chousui  soil_temperature_level_3_kaihua   \n",
       "0                          287.263466                       288.780851  \\\n",
       "1                          287.616328                       289.032764   \n",
       "2                          287.204259                       288.763693   \n",
       "3                          286.732262                       288.209019   \n",
       "4                          288.378758                       289.948855   \n",
       "..                                ...                              ...   \n",
       "97                         287.740673                       289.256381   \n",
       "98                         288.739608                       290.273988   \n",
       "99                         287.779530                       289.413163   \n",
       "100                        287.141116                       288.584741   \n",
       "101                        288.002027                       289.590761   \n",
       "\n",
       "     soil_temperature_level_3_guanjiang  ...  VDVI_shouhuo  WDRVI_bajie   \n",
       "0                            290.517675  ...      0.006043    -0.270294  \\\n",
       "1                            290.784441  ...      0.010118    -0.220879   \n",
       "2                            290.271875  ...      0.038036    -0.273682   \n",
       "3                            290.136585  ...      0.017507    -0.305916   \n",
       "4                            291.813767  ...      0.009523    -0.155182   \n",
       "..                                  ...  ...           ...          ...   \n",
       "97                           291.034992  ...      0.014550    -0.222630   \n",
       "98                           292.107970  ...      0.006859    -0.049587   \n",
       "99                           290.932713  ...      0.021031     0.082507   \n",
       "100                          290.356689  ...      0.027628    -0.102704   \n",
       "101                          291.427115  ...      0.035895    -0.356993   \n",
       "\n",
       "     WDRVI_yunsui  WDRVI_chousui  WDVI_bajie  WDVI_yunsui  WDVI_chousui   \n",
       "0        0.007577       0.038094    0.312649     0.346212      0.380784  \\\n",
       "1       -0.005646       0.020097    0.272293     0.321097      0.313054   \n",
       "2       -0.141936      -0.176015    0.255247     0.291260      0.276982   \n",
       "3       -0.086524       0.009030    0.277992     0.313744      0.335973   \n",
       "4        0.049857       0.027083    0.329961     0.356682      0.342203   \n",
       "..            ...            ...         ...          ...           ...   \n",
       "97      -0.047730      -0.033628    0.276777     0.307460      0.306785   \n",
       "98       0.177212       0.143995    0.356591     0.372768      0.380640   \n",
       "99       0.214809       0.149694    0.389395     0.347135      0.345173   \n",
       "100      0.058565       0.005744    0.309025     0.317261      0.348503   \n",
       "101     -0.162055      -0.172850    0.260427     0.290878      0.283795   \n",
       "\n",
       "     WDVI_kaihua  wet_kaihua           亩产  \n",
       "0       0.355850   -0.090609  7467.000138  \n",
       "1       0.308834   -0.088144  5972.666001  \n",
       "2       0.277341   -0.107184  5687.791498  \n",
       "3       0.327689   -0.066776  7959.597222  \n",
       "4       0.353976   -0.074036  7662.496889  \n",
       "..           ...         ...          ...  \n",
       "97      0.308283   -0.090942  6613.422500  \n",
       "98      0.392420   -0.074186  7579.469809  \n",
       "99      0.381115   -0.079914  5754.140236  \n",
       "100     0.307512   -0.097445  5467.128359  \n",
       "101     0.284526   -0.095178  6637.200003  \n",
       "\n",
       "[102 rows x 95 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75eb75d7-b0ce-4b35-98b5-91a2ab2470aa",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "### 数据处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bb20839c-6340-44b1-a9e7-3c0c367273c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "删除特征的个数： 0\n",
      "出错特征的个数： 0\n",
      "删除的特征： []\n"
     ]
    }
   ],
   "source": [
    "#############删除缺失率高于05的特征\n",
    "null_list = []\n",
    "error_list = []\n",
    "for col in d.columns:\n",
    "    try:\n",
    "        null_list.append([col,d[col].isnull().sum() / d.shape[0]])\n",
    "    except:\n",
    "        error_list.append(col)\n",
    "null_df = pd.DataFrame(null_list,columns = ['特征名称','缺失率'])\n",
    "del_cols = null_df['特征名称'][null_df['缺失率'] >= 0.5]\n",
    "print('删除特征的个数：',len(del_cols))\n",
    "print('出错特征的个数：',len(error_list))\n",
    "print('删除的特征：',del_cols.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "aba9ae28-97f8-4fec-bc66-dd5fbfd318ec",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "删除特征的个数： 0\n",
      "出错特征的个数： 0\n",
      "删除的特征： []\n"
     ]
    }
   ],
   "source": [
    "#############表格中用绿色标记表示删除同值比例高的特征\n",
    "tz_list = []\n",
    "error_list = []\n",
    "for col in d.columns:\n",
    "    try:\n",
    "        tz_list.append([col,d[d[col] == d[col].mode()[0]].shape[0] / (d.shape[0] - d[col].isnull().sum())])\n",
    "    except:\n",
    "        error_list.append(col)\n",
    "tz_df = pd.DataFrame(tz_list,columns = ['特征名称','同值比例'])\n",
    "del_cols = tz_df['特征名称'][tz_df['同值比例'] >= 0.9]\n",
    "print('删除特征的个数：',len(del_cols))\n",
    "print('出错特征的个数：',len(error_list))\n",
    "print('删除的特征：',del_cols.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4032c90c-2e9f-47ff-9b43-383d7419a1d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "d=d.drop(columns=['GNDVI_kaihua','NDVI_chousui','OSAVI_chousui',\n",
    "                   'SVAI_chousui','SR_chousui','NAME'])\n",
    "dd=dd.drop(columns=['GNDVI_kaihua','NDVI_chousui','OSAVI_chousui',\n",
    "                   'SVAI_chousui','SR_chousui','NAME'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4a117973-8634-4679-8251-f818d42ab8c0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#d=d.drop(columns=['GNDVI_chousui','SR_yunsui','WDRVI_chousui','SVAI_chousui','SR_chousui'])\n",
    "#dd=dd.drop(columns=['GNDVI_chousui','SR_yunsui','WDRVI_chousui','SVAI_chousui','SR_chousui'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "299d2aaa-7024-4510-a82e-7a8c7e2bce87",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "### 全部入模"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0fc106ad-358a-451e-bbdf-57729747d20a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "x_train =d.drop(columns = [\"亩产\"])\n",
    "y_train = d['亩产']\n",
    "x_test =dd.drop(columns = [\"亩产\"])\n",
    "y_test = dd['亩产']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ef9e4a27-8669-49ea-b699-edba291bb268",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "model = xgb.XGBRegressor(n_estimators=150,\n",
    "                                 max_depth=5,\n",
    "                                 learning_rate=0.1,\n",
    "                                 \n",
    "                                 random_state=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "44180510-e0eb-449d-bc96-8b985e0ca0c3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "xgboost_model_fit = model.fit(x_train, y_train)\n",
    "y_pred_bo = xgboost_model_fit.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "136d8f93-7a6c-4694-bbb0-cbc45073abd5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>平均绝对误差（MAPE）</th>\n",
       "      <th>平均绝对误差（MAE）</th>\n",
       "      <th>均方根误差（RMSE）</th>\n",
       "      <th>决定系数（R^2）</th>\n",
       "      <th>解释方差</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bo-xgboost</th>\n",
       "      <td>0.095235</td>\n",
       "      <td>557.41642</td>\n",
       "      <td>704.495195</td>\n",
       "      <td>0.724536</td>\n",
       "      <td>0.724593</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            平均绝对误差（MAPE）  平均绝对误差（MAE）  均方根误差（RMSE）  决定系数（R^2）      解释方差\n",
       "bo-xgboost      0.095235    557.41642   704.495195   0.724536  0.724593"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 平均绝对百分比误差\n",
    "mape=mean_absolute_percentage_error(y_test,y_pred_bo)\n",
    "# 计算均方根误差（RMSE）\n",
    "rmse = mean_squared_error(y_test, y_pred_bo, squared=False)\n",
    "# 计算平均绝对误差（MAE）\n",
    "mae = mean_absolute_error(y_test, y_pred_bo)\n",
    "# 计算决定系数（R^2）\n",
    "r2 = r2_score(y_test, y_pred_bo)\n",
    "# 计算解释方差分\n",
    "explained_var = explained_variance_score(y_test, y_pred_bo)\n",
    "\n",
    "results1 = pd.DataFrame()\n",
    "results1['平均绝对误差（MAPE）'] = [mape]\n",
    "results1['平均绝对误差（MAE）'] = [mae]\n",
    "results1['均方根误差（RMSE）'] = [rmse]\n",
    "results1['决定系数（R^2）'] = [r2]\n",
    "results1['解释方差'] = [explained_var]\n",
    "results1.index = ['xgboost']\n",
    "results1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "43a37742-b0a1-4ac8-9f09-39d1fe7992bf",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y_test</th>\n",
       "      <th>y_pred</th>\n",
       "      <th>precent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7467.000138</td>\n",
       "      <td>7330.205566</td>\n",
       "      <td>-1.83%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5972.666001</td>\n",
       "      <td>7011.463867</td>\n",
       "      <td>17.39%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5687.791498</td>\n",
       "      <td>5513.786133</td>\n",
       "      <td>-3.06%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7959.597222</td>\n",
       "      <td>7300.735352</td>\n",
       "      <td>-8.28%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7662.496889</td>\n",
       "      <td>7099.267578</td>\n",
       "      <td>-7.35%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>6613.422500</td>\n",
       "      <td>7029.854004</td>\n",
       "      <td>6.30%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>7579.469809</td>\n",
       "      <td>7070.958008</td>\n",
       "      <td>-6.71%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>5754.140236</td>\n",
       "      <td>7200.633301</td>\n",
       "      <td>25.14%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>5467.128359</td>\n",
       "      <td>5828.631836</td>\n",
       "      <td>6.61%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>6637.200003</td>\n",
       "      <td>6168.673340</td>\n",
       "      <td>-7.06%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>102 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          y_test       y_pred precent\n",
       "0    7467.000138  7330.205566  -1.83%\n",
       "1    5972.666001  7011.463867  17.39%\n",
       "2    5687.791498  5513.786133  -3.06%\n",
       "3    7959.597222  7300.735352  -8.28%\n",
       "4    7662.496889  7099.267578  -7.35%\n",
       "..           ...          ...     ...\n",
       "97   6613.422500  7029.854004   6.30%\n",
       "98   7579.469809  7070.958008  -6.71%\n",
       "99   5754.140236  7200.633301  25.14%\n",
       "100  5467.128359  5828.631836   6.61%\n",
       "101  6637.200003  6168.673340  -7.06%\n",
       "\n",
       "[102 rows x 3 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test_array = np.array(y_test)\n",
    "\n",
    "y_pred_series = pd.Series(y_pred_bo, name='y_pred')\n",
    "y_test_series = pd.Series(y_test_array, name='y_test')\n",
    "dftest= pd.concat([y_test_series, y_pred_series], axis=1)\n",
    "dftest['precent'] = ((dftest['y_pred'] - dftest['y_test']) / dftest['y_test']) * 100\n",
    "dftest['precent'] = dftest['precent'].apply(lambda x: '{:.2f}%'.format(x))\n",
    "dftest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6b1994e2-af3b-478a-992d-e4128e188f91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_test_sorted = np.sort(y_test)\n",
    "y_pred_sorted = y_pred_bo[np.argsort(y_test)]\n",
    "\n",
    "# 绘制折线对比图\n",
    "plt.plot(range(len(y_test_sorted)), y_test_sorted, label='Actual')\n",
    "plt.plot(range(len(y_pred_sorted)), y_pred_sorted, label='Predicted',linestyle='--')\n",
    "#plt.xlabel('Index')\n",
    "plt.ylabel('Value',fontsize=16)\n",
    "plt.title('Actual vs. Predicted (XGBoost)',fontsize=16)\n",
    "plt.legend()\n",
    "plt.ylim(0, 13000)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "df4a379a-10bb-4f49-8b5f-1490cbda5d25",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "plt.scatter(y_test, y_pred_bo, alpha=0.5, label='Data Points')\n",
    "plt.xlabel('Actual Values')\n",
    "plt.ylabel('Predicted Values')\n",
    "plt.title('bh-XGBoost Scatter Plot of Actual vs. Predicted Values')\n",
    "\n",
    "# 添加对角线\n",
    "max_value = max(max(y_test), max(y_pred_bo))\n",
    "min_value = min(min(y_test), min(y_pred_bo))\n",
    "plt.plot([min_value, max_value], [min_value, max_value], color='red', linestyle='--', linewidth=2, label='Perfect Prediction')\n",
    "\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3896133d-f939-4563-8d25-5aed0e0b5413",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
